A Qualitative, Patient-led Exploration of Vaccination Communication Preferences Among a Diverse Sample of Pregnant, Postpartum, Breastfeeding Canadians
Bibliographic record
Abstract
BACKGROUND: Vaccination during pregnancy is recommended to protect pregnant individuals and their fetus from vaccine-preventable diseases and to protect infants during the vulnerable postnatal period. However, vaccine uptake in pregnancy remains low. This study explores how pregnant, postpartum and breastfeeding individuals living in Canada prefer to communicate about vaccination during pregnancy. METHODS: We used peer-to-peer, patient-oriented research to conduct an exploratory qualitative descriptive study using focus groups and semistructured in-depth interviews to enquire about vaccination in pregnancy communication preferences, including preferred provider and communication timing. We coded deductively using direct content analysis and inductively while remaining sensitive to themes arising during the interviews. RESULTS: Fourteen individuals from diverse cultural backgrounds living in Canada who self-identified as women and either as being pregnant, recently postpartum, or breastfeeding participated. Most preferred a participatory approach to vaccine communications combined with clear guidance. A trusted relationship with their provider mattered more than the healthcare provider's profession. Participants wanted to discuss vaccines early and often to allow them time to find answers and discuss with their partners before making decisions. Participants also shared the importance of mutual respect, maintaining their autonomy and not feeling forced or coerced. CONCLUSIONS: Pregnant individuals want to play an active role in decision-making regarding vaccination during pregnancy. Their preference is to have open communication with familiar, trusted providers where they can express their questions and receive a clear recommendation which takes into consideration their unique circumstances so they can evaluate their options before making a decision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".